In this era which is experiencing rapid advancement of the information technologies, there are a lot of methods available for the user to acquire information, one of which is to recommend information to users by a processing system according to the correlations of data. In terms of the recommending method currently adopted, usually users' behavior data is represented in a matrix format and then recommend information to the users according to the evaluation results.
Specifically, assume that five kinds of commodity items A, B, C, D and E are sold in a supermarket. In this case, the users' behavior data is commodity items purchased. For example, commodity items and accumulated amounts purchased by a consumer I in this supermarket are: 5 pieces of item A, 3 pieces of item C and 1 piece of item E, so the purchase data corresponding to the consumer I may be represented as {A, B, C, D, E}={5, 0, 3, 0, 1}. Similarly, assume that commodity items and accumulated amounts purchased by another consumer II in this supermarket are represented as {A, B, C, D, E}={4, 5, 0, 0, 1}. Then for the consumer I, the item B is not purchased by the consumer I but has been purchased by the consumer II. Therefore, the supermarket may recommend the item B to the consumer I as a recommended purchase item for the consumer I. On the other hand, for the consumer II, the item C is not purchased by the consumer II but has been purchased by the consumer I, so the supermarket may recommend the item C to the consumer II as a recommended purchase item for the consumer II.
However, this recommending method is unable to calculate information correlations between consumers if there are more than two consumers. This causes consumers purchasing different commodities to be recommended to each other and leads to significant degradation in effectiveness of the recommended information. Furthermore, the information obtained through this recommending method is only a result of accumulating the consumer data and, instead of distinguishing the consumer information by time, this recommending method evaluates all pieces of consumer information simultaneously. Even when a great change takes place in a consumer′ life, this recommending method is unable to know the great change through analysis.
Accordingly, an urgent need exists in the art to provide a solution that can improve the information correlations among the users and information effectiveness among the pieces of recommended information and that can take into account the differences in different time periods of the users' behavior information (e.g., transaction data, activity data, purchased items or the like).